Unit activity in particular deep neural networks (DNNs) are remarkably similar to the
neuronal population responses to static images along the primate ventral visual cortex …

We characterized the generalization capabilities of DNN-based encoding models when
predicting neuronal responses from the visual cortex. We collected\textit {MacaqueITBench} …

Object recognition relies on inferior temporal (IT) cortical neural population representations
that are themselves computed by a hierarchical network of feedforward and recurrently …

Hierarchical models of primate visual cortex (eg neocognitron/HMAX) have been shown to
perform as well or better than other computer vision approaches in object identification …

How well do deep neural networks fare as models of mouse visual cortex? A majority of
research to date suggests results far more mixed than those produced in the modeling of …

Specific deep artificial neural networks (ANNs) are the current best models of ventral visual
processing and object recognition behavior in monkeys. We here explore whether models of …

Deep predictive models of neuronal activity have recently enabled several new discoveries
about the selectivity and invariance of neurons in the visual cortex. These models learn a …

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …

Deep neural network models are often taken to be direct models of the hierarchical visual
system; under this framework, benchmarking efforts like BrainScore (Schrimpf et al., 2018) …

What is the representational structure of mouse visual cortex and how is it shaped? Mice
obviously interact with the world and recognize objects but unlike in primates the activity of …